CoChat
Sign Up
Cited by 35Technology Adoption and User BehaviourArtificial Intelligence in EducationTechnology-Enhanced Education Studies

Drivers of Acceptance of Generative AI Through the Lens of the Extended Unified Theory of Acceptance and Use of Technology

Open access
Ayyoub, Abdalkarim, Khlaif, Zuheir, Hamamra, Bilal, Bensalem, Elias, Mitwally, Mohamed, Sanmugam, Mageswaran, Fteiha, Ahmad, Joma, Amjad +3 more·2025·Human Behavior and Emerging Technologies
DOI 10.1155/hbe2/6265087
Open this paper in CoChat — free accountOpen PDF

Abstract

The acceptance and adoption of emerging technologies are crucial for their effective integration. This study examines the factors influencing educators’ acceptance of Generative AI (Gen AI) tools in higher education, guided by the UTAUT model. It also develops a structural model to explore the relationships between UTAUT constructs and behavioral intention (BI) to use Gen AI. Using a quantitative approach, the study collected data through a self‐administered online survey based on prior research findings. The survey gathered responses from 307 educators across various Arab countries who are early adopters of Gen AI in teaching. PLS‐SEM was used to analyze the data. Findings indicate that UTAUT constructs significantly and positively influence educators’ intention to use Gen AI. Additionally, the results highlight the complex role of gender and work experience, revealing diverse perspectives among educators from different countries. This study contributes to the literature by deepening the understanding of technology adoption factors. It also offers theoretical and practical implications for researchers and policymakers in designing strategies to integrate Gen AI into higher education in developing countries.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1155/hbe2/6265087

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Ethical challenges include concerns about bias, opacity, and data privacy, particularly in contexts where student data are increasingly used for analytics and monitoring.

    “Ethical challenges include concerns about bias, opacity, and data privacy, particularly in contexts where student data are increasingly used for analytics and monitoring (Ayyoub et al., 2025;Floridi, 2019).”

    From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Ayyoub et al., 2025

    Section 2.2 identifies privacy and bias as ethical concerns in higher education and mentions predictive analytics and identifying at-risk students. It does not discuss opacity or say that student data are increasingly used for analytics and monitoring, so those parts of the attributed claim are unsupported. Page numbers are unavailable in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Teachers' reflections align with phenomenological research highlighting the importance of teacher readiness and professional learning in adoption of emerging technologies.

    “Such reflections align with phenomenological research highlighting the importance of teacher readiness and professional learning in the adoption of emerging technologies (Ayyoub et al., 2025).”

    From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Ayyoub et al., 2025

    The Introduction says professional development and training can enhance educators’ readiness to use generative AI. However, the Abstract describes a quantitative survey analyzed with PLS-SEM, not phenomenological research. The source supports the readiness and professional-learning point, but not the characterization of its method. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Teachers' interpretations position them as active agents in digital transformation rather than passive technology users.

    “These interpretations position teachers as active agents in digital transformation rather than passive technology users, offering an important contribution to a literature largely focused on higher education (Ayyoub et al., 2025;Khlaif et al., 2024).”

    From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Ayyoub et al., 2025

    The Conclusion (p. unavailable) describes educators’ adoption decisions as “intentional choices,” supporting the idea that they are not merely passive users. But the study examines GenAI acceptance among Arab higher-education educators; it does not examine the teachers’ interpretations invoked in the claim or explicitly frame them as agents of digital transformation. The Context of the Study (p. unavailable) confirms that the paper is an example of higher-education research. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • This pattern mirrors international research emphasizing that sustainable AI integration depends not only on technological availability but also on teacher preparedness and organizational support.

    “This pattern mirrors international research emphasizing that sustainable AI integration depends not only on technological availability but also on teacher preparedness and organizational support (Ayyoub et al., 2025;Pedro et al., 2019).”

    From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Ayyoub et al., 2025

    Section 4.1 recommends educator training and institutional support alongside financial and technical measures for GenAI adoption in Arab higher education. But the study examines early adopters and does not establish that these factors are necessary for sustainable AI integration across settings; Section 5.1 notes that longitudinal adoption was not studied. Page numbers are unavailable in our copy. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The findings support existing evidence that sustainable AI integration depends as much on systemic and organizational conditions as on technological capability.

    “In this regard, the findings support existing evidence that sustainable AI integration depends as much on systemic and organizational conditions as on technological capability (Ayyoub et al., 2025;Huang et al., 2021;Greenhow et al., 2020;Kuleto et al., 2021).”

    From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Ayyoub et al., 2025

    The Discussion (section 4) says successful, responsible GenAI integration in higher education requires technical support, financial accessibility, and human-centered design; the Conclusion (section 5) also calls for technological, financial, and cultural support. These passages support the importance of organizational conditions alongside technology, but the study concerns educators’ GenAI acceptance in Arab higher education, not the long-term sustainability of AI integration generally or an equal weighting of those factors. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

1 retracted

Flagged first, then by citations

Research works printed but not linked: 50

  1. Z. Lv, "Generative Artificial Intelligence in the Metaverse Era," Cognitive Robotics 3 (2023): 208-217, https://doi.org/10.1016/ j.cogr.2023.06.001. · DOI
  2. J. M. Berg, M. Raj, and R. Seamans, "Capturing Value From Artificial Intelligence," Academy of Management Discoveries 9, no. 4 (2023): 424-428, https://doi.org/10.5465/amd. 2023.0106. · DOI · Paper page
  3. R. Peres, M. Schreier, D. Schweidel, and A. Sorescu, "On ChatGPT and Beyond: How Generative Artificial Intelligence May Affect Research, Teaching, and Practice," International Journal of Research in Marketing 40, no. 2 (2023): 269-275, https://doi.org/10.1016/j.ijresmar.2023.03.001. · DOI · Paper page
  4. V. Venkatesh, "Adoption and Use of AI Tools: A Research Agenda Grounded in UTAUT," Annals of Operations Research 308, no. 1-2 (2022): 641-652, https://doi.org/10.1007/s10479- 020-03918-9. 10 Human Behavior and Emerging Technologies · DOI
  5. A. M. Al-Zahrani and T. M. Alasmari, "A Comprehensive Analysis of AI Adoption, Implementation Strategies, and Challenges in Higher Education Across the Middle East and North Africa (MENA) Region," Education and Information Technologies 30, no. 8 (2025): 11339-11389, https://doi.org/ 10.1007/s10639-024-13300-y. · DOI · Paper page
  6. N. M. N. Abubaker, S. Kashani, A. M. Alshalwy, and A. Garib, "Reshaping Higher Education in MENA with Generative AI: A Systematic Review," in Emerging Technologies Transforming Higher Education: Instructional Design and Student Success (IGI Global Scientific Publishing, 2025), 231-256.
  7. L. Du and B. Lv, "Factors Influencing Students' Acceptance and Use Generative Artificial Intelligence in Elementary Edu- cation: An Expansion of the UTAUT Model," Educational Information Technology 29, no. 18 (2024): 24715-24734, https://doi.org/10.1007/s10639-024-12835-4. · DOI
  8. H. Zhang and T. Wareewanich, "A Study of the Factors Influencing Teachers' Willingness to Use Generative Artificial Intelligence Based on the UTAUT Model," International Jour- nal of Interactive Mobile Technologies 18, no. 6 (2024): 126- 142, https://doi.org/10.3991/ijim.v18i06.47991. · DOI · Paper page
  9. A. Al-Azawei and A. Alowayr, "Predicting the Intention to Use and Hedonic Motivation for Mobile Learning: A Comparative Study in Two Middle Eastern Countries," Technology in Society 62 (2020): 101325, https://doi.org/10.1016/j.techsoc.2020.101325. · DOI
  10. A. F. Alkhwaldi, "Understanding Learners' Intention Toward Metaverse in Higher Education Institutions From a Develop- ing Country Perspective: UTAUT and ISS Integrated Model," Kybernetes 53, no. 12 (2024): 6008-6035, https://doi.org/ 10.1108/K-03-2023-0459. · DOI
  11. H. Al Halbusi, K. Al-Sulaiti, F. Abdelfattah, A. B. Ahmad, and S. Hassan, "Understanding Consumers' Adoption of e- Pharmacy in Qatar: Applying the Unified Theory of Accep- tance and Use of Technology," Journal of Science and Technol- ogy Policy Management 16, no. 3 (2025): 479-505, https:// doi.org/10.1108/JSTPM-03-2023-0042. · DOI
  12. R. Assaf, M. Omar, Y. Saleh, H. Attar, N. T. Alaqra, and M. Kanan, "Assessing the Acceptance for Implementing Arti- ficial Intelligence Technologies in the Governmental Sector: An Empirical Study," Engineering, Technology & Applied Sci- ence Research 14, no. 6 (2024): 18160-18170, https://doi.org/ 10.48084/etasr.8711. · DOI
  13. F. Hamad, A. Shehata, and N. Al Hosni, "Predictors of Blended Learning Adoption in Higher Education Institutions in Oman: Theory of Planned Behavior," International Journal of Educa- tional Technology in Higher Education 21, no. 1 (2024): 1-28, https://doi.org/10.1186/s41239-024-00443-8. · DOI
  14. V. Venkatesh, J. Y. Thong, and X. Xu, "Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead," Journal of the association for Information Systems 17, no. 5 (2016): 328-376, https://doi.org/10.17705/1jais.00428. · DOI · Paper page
  15. Z. Khlaif, "Teachers' Perceptions of Factors Affecting Their Adoption and Acceptance of Mobile Technology in K-12 Set- tings," Computers in the Schools 35, no. 1 (2018): 49-67, https://doi.org/10.1080/07380569.2018.1428001. · DOI
  16. M. S. Venkatesh and G. S. V. Raghavan, "An Overview of Microwave Processing and Dielectric Properties of Agri-Food Materials," Biosystems Engineering 88, no. 1 (2004): 1-18, https://doi.org/10.1016/j.biosystemseng.2004.01.007. · DOI
  17. J.-P. Cabrera-Sánchez, Á. F. Villarejo-Ramos, F. Liébana- Cabanillas, and A. A. Shaikh, "Identifying Relevant Segments of AI Applications Adopters-Expanding the UTAUT2's Vari- ables," Telematics and Informatics 58 (2021): 101529, https:// doi.org/10.1016/j.tele.2020.101529. · DOI · Paper page
  18. S. Chatterjee, R. Chaudhuri, D. Vrontis, A. Thrassou, and S. K. Ghosh, "Adoption of Artificial Intelligence-Integrated CRM Systems in Agile Organizations in India," Technological Fore- casting and Social Change 168 (2021): 120783, https:// doi.org/10.1016/j.techfore.2021.120783. · DOI
  19. R. Jain, N. Garg, and S. N. Khera, "Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model," Frontiers in Psychology 13 (2022): 893691, https://doi.org/10.3389/fpsyg.2022.893691. · DOI
  20. V. Venkatesh, D. C. Ganster, S. W. Schuetz, and T. A. Sykes, "Risks and Rewards of conscientiousness During the COVID-19 Pandemic," Journal of Applied Psychology 106, no. 5 (2021): 643-656, https://doi.org/10.1037/apl0000919. · DOI
  21. O. A. Gansser and C. S. Reich, "A New Acceptance Model for Artificial Intelligence With Extensions to UTAUT2: An Empirical Study in Three Segments of Application," Technol- ogy in Society 65 (2021): 101535, https://doi.org/10.1016/ j.techsoc.2021.101535. · DOI
  22. K. Kavitha and V. P. Joshith, "Factors Shaping the Adoption of AI Tools Among Gen Z: An Extended UTAUT2 Model Inves- tigation Using CB-SEM," Bulletin of Science, Technology & Society 44, no. 1-2 (2024): 12-32, https://doi.org/10.1177/ 02704676241283362. · DOI
  23. J. E. Andrews, H. Ward, and J. Yoon, "UTAUT as a Model for Understanding Intention to Adopt AI and Related Technolo- gies Among Librarians," Journal of Academic Librarianship 47, no. 6 (2021): 102437, https://doi.org/10.1016/j.acalib. 2021.102437. · DOI
  24. W. Wu, B. Zhang, S. Li, and H. Liu, "Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory," Frontiers in Psychology 13 (2022): 870777, https://doi.org/10.3389/fpsyg.2022.870777. 11 Human Behavior and Emerging Technologies · DOI
  25. A. J. Guerrero-Quiñonez, M. C. Bedoya-Flores, E. F. Mos- quera-Quiñonez, Á. E. Mesías-Simisterra, and J. V. Bautista- Sánchez, "Artificial Intelligence and its Scope in Latin Ameri- can Higher Education," Ibero-American Journal of Education & Society Research 3, no. 1 (2023): 264-271, https://doi.org/ 10.56183/iberoeds.v3i1.627. · DOI
  26. I. Drach, O. Petroye, O. Borodiyenko, et al., "The Use of Arti- ficial Intelligence in Higher Education," International Scien- tific Journal of Universities and Leadership 15 (2023): 66-82, https://doi.org/10.31874/2520-6702-2023-15-66-82. · DOI
  27. A. V. N. S. S. Thimmanna, M. S. Naik, S. Radhakrishnan, and A. Sharma, "Personalized Learning Paths: Adapting Education With AI-Driven Curriculum," European Economic Letters (EEL) 14, no. 1 (2024): 31-40, https://doi.org/10.52783/ eel.v14i1.993. · DOI
  28. R. Tank, A. Diaz, M. T. Ashford, et al., "Examining Demo- graphic Factors, Psychosocial Wellbeing and Cardiovascular Health in Subjective Cognitive Decline in the Brain Health Registry Cohort," Journal of Prevention of lzheimer's Disease 11, no. 3 (2024): 787-797, https://doi.org/10.14283/ jpad.2024.39. · DOI
  29. T. K. Vashishth, V. Sharma, K. K. Sharma, B. Kumar, R. Panwar, and S. Chaudhary, "AI-Driven Learning Analytics for Personalized Feedback and Assessment in Higher Educa- tion," in Using Traditional Design Methods to Enhance AI- Driven Decision Making (IGI Global, 2024), 206-230, https:// doi.org/10.4018/979-8-3693-0639-0.ch009. · DOI
  30. W. M. Villamar Solís, C. A. García Ríos, C. E. Cevallos Her- mida, J. L. A. Alencastre, and J. H. Tovalin-Ahumada, "The Impact of Artificial Intelligence on Higher Education: A Socio- logical Perspective," Journal of Namibian Studies: History Pol- itics Culture 34 (2023): 3284.
  31. S. Sallu, N. M. Sianturi, B. Purwoko, Y. Herliansyah, and M. A. Manuhutu, "Learning in Higher Education Based on Artificial Intelligence (AI) With Case Based Reasoning (CBR)," Journal of Namibian Studies: History Politics Culture 34 (2023): 1049- 1064.
  32. T. Wang, B. D. Lund, A. Marengo, et al., "Exploring the Poten- tial Impact of Artificial Intelligence (AI) on International Stu- dents in Higher Education: Generative AI, Chatbots, Analytics, and International Student Success," Applied Sciences 13, no. 11 (2023): 6716, https://doi.org/10.3390/app13116716. · DOI
  33. R. Raman, S. Mandal, P. Das, T. Kaur, J. P. Sanjanasri, and P. Nedungadi, "Exploring University Students' Adoption of ChatGPT Using the Diffusion of Innovation Theory and Sen- timent Analysis With Gender Dimension," Human Behavior and Emerging Technologies 2024 (2024): 21, 3085910, https:// doi.org/10.1155/2024/3085910. · DOI
  34. K. Qu and X. Wu, "ChatGPT as a CALL Tool in Language Education: A Study of Hedonic Motivation Adoption Models in English Learning Environments," in Education and Infor- mation Technologies (Springer, 2024), 1-33.
  35. C. M. Chang, L. W. Liu, H. C. Huang, and H. H. Hsieh, "Fac- tors Influencing Online Hotel Booking: Extending UTAUT2 With Age, Gender, and Experience as Moderators," Informa- tion 10, no. 9 (2019): 281, https://doi.org/10.3390/ info10090281. · DOI
  36. K. K. Arthur, R. K. Bannor, P. Darko, O. Hlortu, and S. Adom, "Supply Chain Intelligence: Integration of Emerging Digital Innovations to Promote Sustainable Supply Chain Practices" Available at SSRN 5167010.
  37. Z. N. Khlaif and S. Salha, "Exploring the Factors Influencing Mobile Technology Integration in Higher Education," Tech- nology, Pedagogy and Education 31, no. 3 (2022): 347-362, https://doi.org/10.1080/1475939X.2022.2052949. · DOI
  38. R. Mishra, R. K. Singh, and J. Paul, "Factors Influencing Behavioural Intention to Avail Omnichannel Service Among Gen Y Consumers," Benchmarking: An International Journal 32, no. 3 (2025): 1017-1044, https://doi.org/10.1108/BIJ-05- 2023-0333. · DOI
  39. V. Venkatesh, J. Y. L. Thong, and X. Xu, "Consumer Accep- tance and Use of Information Technology: Extending the Uni- fied Theory of Acceptance and Use of Technology," MIS Quarterly 36, no. 1 (2012): 157-178, https://doi.org/10.2307/ 41410412. · DOI · Paper page
  40. C. M. Ringle, M. Sarstedt, N. Sinkovics, and R. R. Sinkovics, "A Perspective on Using Partial Least Squares Structural Equation Modelling in Data Articles," Data in Brief 48 (2023): 109074, https://doi.org/10.1016/j.dib.2023.109074. · DOI
  41. J. Hair and A. Alamer, "Partial Least Squares Structural Equa- tion Modeling (PLS-SEM) in Second Language and Education Research: Guidelines Using An Applied Example," Research ethods in Applied Linguistics 1, no. 3 (2022): 100027, https:// doi.org/10.1016/j.rmal.2022.100027. · DOI · Paper page
  42. T. Dijkstra, K. Miwa, B. Brummelhuis, M. Sappelli, and H. Baayen, "How Cross-Language Similarity and Task Demands Affect Cognate Recognition," Journal of Memory and Language 62, no. 3 (2010): 284-301, https://doi.org/ 10.1016/j.jml.2009.12.003. · DOI
  43. T. K. Dijkstra, "PLS' Janus Face -Response to Professor Rig- don's 'Rethinking Partial Least Squares Modeling: In Praise of Simple Methods'," Long Range Planning 47, no. 3 (2014): 146-153, https://doi.org/10.1016/j.lrp.2014.02.004. · DOI
  44. T. K. Dijkstra and J. Henseler, "Consistent Partial Least Squares Path Modeling," MIS Quarterly 39, no. 2 (2015): 297-316, https://doi.org/10.25300/MISQ/2015/39.2.02. · DOI · Paper page
  45. M. Sarstedt, J. F. Hair, M. Pick, B. D. Liengaard, L. Radomir, and C. M. Ringle, "Progress in Partial Least Squares Structural 12 Human Behavior and Emerging Technologies Equation Modeling Use in Marketing Research in the Last Decade," Psychology & Marketing 39, no. 5 (2022): 1035- 1064, https://doi.org/10.1002/mar.21640. · DOI
  46. J. Henseler, C. M. Ringle, and M. Sarstedt, "A New Criterion for Assessing Discriminant Validity in Variance-Based Struc- tural Equation Modeling," Journal of the Academy of Market- ing Science 43, no. 1 (2015): 115-135, https://doi.org/ 10.1007/s11747-014-0403-8. · DOI · Paper page
  47. J. F. Hair, C. M. Ringle, and M. Sarstedt, "PLS-SEM: Indeed a Silver Bullet," Journal of Marketing Theory and Practice 19, no. 2 (2011): 139-152, https://doi.org/10.2753/MTP1069- 6679190202. · DOI · Paper page
  48. J. Cohen, Statistical Power Analysis for the Behavioral Sciences (Routledge, 2nd edition, 1988), https://doi.org/10.4324/ 9780203771587. · DOI · Paper page
  49. E. M. Rogers, "Diffusion Networks," Networks in the Knowl- edge Economy 10 (2003): https://doi.org/10.1093/oso/ 9780195159509.003.0011. · DOI · Paper page
  50. Z. N. Khlaif, A. Ayyoub, B. Hamamra, et al., "University Teachers' Views on the Adoption and Integration of Genera- tive AI Tools for Student Assessment in Higher Education," Education Sciences 14, no. 10 (2024): 1090, https://doi.org/ 10.3390/educsci14101090. · DOI · Paper page

Cited by

Sorted by citations

Authors
Ayyoub, A., Khlaif, Z., Hamamra, B., Bensalem, E., Mitwally, M., Sanmugam, M., Fteiha, A., Joma, A., Bsharat, T. R. K., Eidah, B. A., & Khaldi, M.
Affiliations
  • An-Najah National University (PS)
  • Northern Border University (SA)
  • University of South Africa (ZA)
  • University of Doha for Science and Technology (QA)
  • Birzeit University (PS)
  • A'Sharqiyah University (OM)
  • International Islamic University Malaysia (MY)
ORCID
Venue
Human Behavior and Emerging Technologies
Publisher
Wiley
Published
2025-01
Volume
2025
Issue
1
ISSN
2578-1863 (ISSN-L)
DOI
10.1155/hbe2/6265087
OpenAlex
W4412662625
Cited by
35
References
17
Citations by year
2026: 29 · 2025: 6
FWCI
35.04
Type
journal article
Language
en
Update notices
correction (2026-03-16)
Open access
Licence
Locations
  • Human Behavior and Emerging Technologies (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Education › Technology-Enhanced Education Studies
Keywords
generative AI, technology acceptance, UTAUT, behavioral intention, educators, higher education, technology adoption, gender differences, work experience, Arab world, early adopters

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review